Tone placement for reference signal optimization

ABSTRACT

Disclosed are techniques for determining tone patterns for transmission of reference signals. One or more parameters associated with communications with a base station over a time duration, may be determined by a user equipment. The user equipment may determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration. The user equipment may then transmit the tone pattern to the base station. The user equipment may then receive the reference signal from the base station, over the tone patterns

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional application Ser. No. 17/142,175, filed Jan. 5, 2021, which is hereby incorporated by reference, in its entirety and for all purposes.

FIELD OF THE DISCLOSURE

Aspects of the disclosure relate generally to wireless communications. In some implementations, examples are described for optimizing tone patterns for transmission of reference signals between a base station and a user equipment (UE) device.

BACKGROUND OF THE DISCLOSURE

Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G networks), a third-generation (3G) high speed data, Internet-capable wireless service, a fourth-generation (4G) service (e.g., Long-Term Evolution (LTE), WiMax), and most recently a fifth-generation (5G) service. There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular Analog Advanced Mobile Phone System (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), the Global System for Mobile communication (GSM), etc.

The fifth-generation (5G) mobile standard calls for higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. The 5G standard (also referred to as “New Radio” or “NR”), according to the Next Generation Mobile Networks Alliance, is designed to provide data rates of several tens of megabits per second to each of tens of thousands of users with, for example, a gigabit connection speeds to tens of users in a common location, such as on an office floor. Several hundreds of thousands of simultaneous connections are to be supported in order to support large sensor deployments. Consequently, there is a need for significantly enhancing the spectral efficiency of 5G mobile communications compared to the current 4G/LTE standard. Furthermore, there is also a corresponding need for enhancing signaling efficiencies and substantially reducing latency compared to current standards.

SUMMARY

The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

Disclosed are systems, apparatuses, methods, and computer-readable media for tone pattern determination and optimization used for transmission of reference signals between a user equipment and a base station.

According to at least one example, a method includes: determining, by a user equipment, one or more parameters associated with communications with a base station over a time duration; determining, by the user equipment and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and transmitting the tone pattern to the base station.

In another example, an apparatus includes one or more memories having computer-readable instructions stored therein, and one or more processors. The one or more processors are configured to: execute the computer-readable instructions to: determine one or more parameters associated with communications with a base station over a time duration; determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and transmit the tone pattern to the base station.

In another example, a non-transitory computer-readable medium is provided that includes stored thereon at least one instruction that, when executed by one or more processors, cause the one or more processors to: determine one or more parameters associated with communications with a base station over a time duration; determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and transmit the tone pattern to the base station.

In another example, an apparatus is provided. The apparatus includes: means for determining, by a user equipment, one or more parameters associated with communications with a base station over a time duration; means for determining, by the user equipment and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and means for transmitting the tone pattern to the base station. In another example, an apparatus in provided. The apparatus includes: one or more memories having computer-readable instructions stored therein, and one or more processors. The one or more processors are configured to execute the computer-readable instructions to: determine one or more parameters associated with communications with user equipment over a time duration; determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user equipment over a future time duration; and transmit the tone pattern to the user equipment.

In another example, a method includes: determining, by a base station, one or more parameters associated with communications with a user device over a time duration; determining, by the base station and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user device over a future time duration; and transmitting, by the base station and to the user device, the reference signal using the tone pattern.

In another example, a non-transitory computer-readable medium is provided that includes stored thereon at least one instruction that, when executed by one or more processors, cause the one or more processors to: determine one or more parameters associated with communications with user equipment over a time duration; determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user equipment over a future time duration; and transmit the tone pattern to the user equipment.

In another example, an apparatus is provided. The apparatus includes: means for determining, by a base station, one or more parameters associated with communications with a user device over a time duration; means for determining, by the base station and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user device over a future time duration; and means for transmitting, by the base station and to the user device, the reference signal using the tone pattern.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are presented to aid in the description of various aspects of the disclosure and are provided solely for illustration of the aspects and not limitation thereof.

FIG. 1 is a diagram illustrating an example wireless communications system, in accordance with some aspects of the present disclosure.

FIGS. 2A and 2B are diagrams illustrating example wireless network structures, in accordance with some aspects of the present disclosure.

FIG. 3 is a diagram illustrating a design of a base station and a User Equipment (UE) device that enable transmission and processing of signals exchanged between the UE and the base station, in accordance with some aspects of the present disclosure.

FIG. 4 is a conceptual diagram illustrating an example of a frame structure, in accordance with some aspects of the present disclosure.

FIG. 5 is a conceptual diagram illustrating an example machine learning model that can be configured to facilitate tone placement optimization, in accordance with some aspects of the present disclosure.

FIG. 6 is a flow chart illustrating an example of a process of training a machine learning algorithm for tone pattern determination, in accordance with some aspects of the present disclosure.

FIG. 7 is a flow chart illustrating an example of a process of communicating a customized tone pattern, in accordance with some aspects of the present disclosure.

FIGS. 8A-B are conceptual diagrams illustrating non-limiting examples of customized irregular tone pattern arrangements, in accordance with some aspects of the present disclosure.

FIG. 9 is a flow chart illustrating an example of a process of communicating a customized tone pattern, in accordance with some aspects of the present disclosure.

FIG. 10 is a flow chart illustrating an example of a process of communicating a customized tone pattern, in accordance with some aspects of the present disclosure.

FIG. 11 is a flow chart illustrating an example of a process of communicating a customized tone pattern, in accordance with some aspects of the present disclosure.

FIG. 12 illustrates components of a user equipment, according to some aspects of the present disclosure.

DETAILED DESCRIPTION

Certain aspects and embodiments of this disclosure are provided below for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure. Some of the aspects and embodiments described herein may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive.

Systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively referred to herein as systems and techniques) are described herein for optimizing a tone pattern for transmission of reference signals between a base station (e.g., base station, new radio, gNodeB, etc.) and a user equipment (UE).

As noted above, 5G mobile standard calls for higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. 5G is expected to support several hundreds of thousands of simultaneous connections. Consequently, there is room to improve the spectral efficiency of 5G mobile communications by enhancing signaling efficiencies and reducing latency. One aspect where such signaling efficiency and reduction in latency can be achieved is the communication of various uplink and downlink reference signals between user equipment and their respective serving base stations.

Reference signals are predefined signals occupying specific resource elements within a time—frequency grid of a resource block and may be exchanged on one or both of downlink and uplink physical communication channels. Each reference signal has been defined by the 3rd Generation Partnership Project (3GPP) for a specific purpose such as channel estimation, phase-noise compensation, acquiring downlink/uplink channel state information, time and frequency tracking, etc.

Example reference signals include, but are not limited to, Channel State Information—Reference Signal (CSI-RS), De-Modulation Reference Signal (DMRS), Sounding Reference Signal (SRS), among others. Some reference signals such as CSI-RS are downlink specific signals, while others such as DMRS are sent both on downlink and uplink communication channels. There are also uplink specific reference signals defined by the 3GPP.

Tone patterns can be defined as specific arrangements of resource elements in a given resource block for transmission of a reference signal. Tone patterns are currently pre-defined in the 5G communication standard and are known to both the user equipment and corresponding base station. In addition, estimated power used for transmission of a given resource element is known to the user equipment and base station. Accordingly, both the user equipment and base station have the necessary information in order to code/decode reference signals and perform corresponding measurements.

Pre-defined tone patterns may not be optimized for all environments. In other words, the arrangement or combination of resource elements used for transmission of a particular reference signal may not be optimized across all possible conditions under which a user equipment and base station may communicate.

The systems and techniques described herein dynamically determine (or configure) optimized tone patterns for transmission of reference signals by one or more base stations and/or by one or more UEs. Determining optimized tone patterns improves signal and spectral efficiency and reduces overhead associated with transmission of reference signals for mobile systems (e.g., in 5G mobile systems). In some examples, as described in more detail below, dynamic determination of tone patterns can be achieved through the use of machine learning models, among various other techniques. For instance, a given condition under which a reference signal is to be communicated from a user equipment to a base station (and/or from a base station to a user equipment) can be provided as input to a machine learning model. Over time, the machine learning model can be trained to associate various conditions with different Resource Elements (REs) best suited for reference signal tone placement in order to achieve an optimized output (e.g., spectral efficiency). Once trained, when such conditions can be provided as input, the machine learning model can process the input and can provide an optimized tone pattern for transmission of the reference signal.

Additional aspects of the present disclosure are described in more detail below with respect to the figures.

According to various aspects, FIG. 1 illustrates an example wireless communications system 100. The wireless communications system 100 (which may also be referred to as a wireless wide area network (WWAN)) can include various base stations 102 and various UEs 104.

As used herein, the terms “user equipment” (UE) and “base station” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, and/or tracking device, etc.), wearable (e.g., smartwatch, smart-glasses, wearable ring, and/or an extended reality (XR) device such as a virtual reality (VR) headset, an augmented reality (AR) headset or glasses, or a mixed reality (MR) headset), vehicle (e.g., automobile, motorcycle, bicycle, etc.), and/or Internet of Things (IoT) device, etc., used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and/or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on IEEE 802.11 communication standards, etc.) and so on.

A base station may operate according to one of several RATs in communication with UEs depending on the network in which it is deployed, and may be alternatively referred to as an access point (AP), a network node, a NodeB (NB), an evolved NodeB (eNB), a next generation eNB (ng-eNB), a New Radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be used primarily to support wireless access by UEs, including supporting data, voice, and/or signaling connections for the supported UEs. In some systems a base station may provide edge node signaling functions while in other systems it may provide additional control and/or network management functions. A communication link through which UEs can send signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). A communication link through which the base station can send signals to UEs is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, or a forward traffic channel, etc.). As used herein the term traffic channel (TCH) can refer to either an uplink, reverse or downlink, and/or a forward traffic channel.

The term “base station” may refer to a single physical transmission-reception point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term “base station” refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference RF signals (or simply “reference signals”) the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

A radio frequency signal or “RF signal” comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

Referring back to FIG. 1 , the base stations 102 can include macro cell base stations (high power cellular base stations) and/or small cell base stations (low power cellular base stations). In an aspect, the macro cell base station may include eNBs and/or ng-eNBs where the wireless communications system 100 corresponds to an LTE network, or gNBs where the wireless communications system 100 corresponds to a NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

In some implementations that support positioning of UEs, a base station may not support wireless access by UEs (e.g., may not support data, voice, and/or signaling connections for UEs), but may instead transmit reference signals to UEs to be measured by the UEs, and/or may receive and measure signals transmitted by the UEs. Such a base station may be referred to as a positioning beacon (e.g., when transmitting signals to UEs) and/or as a location measurement unit (e.g., when receiving and measuring signals from UEs).

The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through backhaul links 122, and through the core network 170 to one or more location servers 172 (which may be part of core network 170 or may be external to core network 170). In addition to other functions, the base stations 102 may perform functions that relate to one or more of transferring user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through the EPC or 5GC) over backhaul links 134, which may be wired and/or wireless.

The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In an aspect, one or more cells may be supported by a base station 102 in each coverage area 110. A “cell” is a logical communication entity used for communication with a base station (e.g., over some frequency resource, referred to as a carrier frequency, component carrier, carrier, band, or the like), and may be associated with an identifier (e.g., a physical cell identifier (PCI), a virtual cell identifier (VCI), a cell global identifier (CGI)) for distinguishing cells operating via the same or a different carrier frequency. In some cases, different cells may be configured according to different protocol types (e.g., machine-type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access for different types of UEs. Because a cell is supported by a specific base station, the term “cell” may refer to either or both of the logical communication entity and the base station that supports it, depending on the context. In addition, because a TRP is typically the physical transmission point of a cell, the terms “cell” and “TRP” may be used interchangeably. In some cases, the term “cell” may also refer to a geographic coverage area of a base station (e.g., a sector), insofar as a carrier frequency can be detected and used for communication within some portion of geographic coverage areas 110.

While neighboring macro cell base station 102 geographic coverage areas 110 may partially overlap (e.g., in a handover region), some of the geographic coverage areas 110 may be substantially overlapped by a larger geographic coverage area 110. For example, a small cell base station 102′ may have a coverage area 110′ that substantially overlaps with the coverage area 110 of one or more macro cell base stations 102. A network that includes both small cell and macro cell base stations may be known as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG).

The communication links 120 between the base stations 102 and the UEs 104 may include uplink (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and/or downlink (also referred to as forward link) transmissions from a base station 102 to a UE 104. The communication links 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links 120 may be through one or more carrier frequencies. Allocation of carriers may be asymmetric with respect to downlink and uplink (e.g., more or less carriers may be allocated for downlink than for uplink).

The wireless communications system 100 may further include a wireless local area network (WLAN) access point (AP) 150 in communication with WLAN stations (STAs) 152 via communication links 154 in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in an unlicensed frequency spectrum, the WLAN STAs 152 and/or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen before talk (LBT) procedure prior to communicating in order to determine whether the channel is available. In some examples, the wireless communications system 100 can include devices (e.g., UEs etc.) that communicate with one or more UEs 104, base stations 102, APs 150, etc. utilizing the ultra-wideband (UWB) spectrum. The UWB spectrum can range from 3.1 to 10.5 GHz.

The small cell base station 102′ may operate in a licensed and/or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102′ may employ LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP 150. The small cell base station 102′, employing LTE and/or 5G in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network. NR in unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MulteFire.

The wireless communications system 100 may further include a millimeter wave (mmW) base station 180 that may operate in mmW frequencies and/or near mmW frequencies in communication with a UE 182. Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. Radio waves in this band may be referred to as a millimeter wave. Near mmW may extend down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also referred to as centimeter wave. Communications using the mmW and/or near mmW radio frequency band have high path loss and a relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and/or receive) over an mmW communication link 184 to compensate for the extremely high path loss and short range. Further, it will be appreciated that in alternative configurations, one or more base stations 102 may also transmit using mmW or near mmW and beamforming. Accordingly, it will be appreciated that the foregoing illustrations are merely examples and should not be construed to limit the various aspects disclosed herein.

Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omni-directionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device(s). To change the directionality of the RF signal when transmitting, a network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that are broadcasting the RF signal. For example, a network node may use an array of antennas (referred to as a “phased array” or an “antenna array”) that creates a beam of RF waves that can be “steered” to point in different directions, without actually moving the antennas. Specifically, the RF current from the transmitter is fed to the individual antennas with the correct phase relationship so that the radio waves from the separate antennas add together to increase the radiation in a desired direction, while canceling to suppress radiation in undesired directions.

In receiving beamforming, the receiver uses a receive beam to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and/or adjust the phase setting of an array of antennas in a particular direction to amplify (e.g., to increase the gain level of) the RF signals received from that direction. Thus, when a receiver is said to beamform in a certain direction, it means the beam gain in that direction is high relative to the beam gain along other directions, or the beam gain in that direction is the highest compared to the beam gain of other beams available to the receiver. This results in a stronger received signal strength, (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), etc.) of the RF signals received from that direction.

Receive beams may be spatially related. A spatial relation means that parameters for a transmit beam for a second reference signal can be derived from information about a receive beam for a first reference signal. For example, a UE may use a particular receive beam to receive one or more reference downlink reference signals (e.g., positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signal (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), primary synchronization signals (PSS), secondary synchronization signals (SSS), synchronization signal blocks (SSBs), etc.) from a base station. The UE can then form a transmit beam for sending one or more uplink reference signals (e.g., uplink positioning reference signals (UL-PRS), sounding reference signal (SRS), demodulation reference signals (DMRS), PTRS, etc.) to that base station based on the parameters of the receive beam.

Note that a “downlink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. If the UE is forming the downlink beam, however, it is a receive beam to receive the downlink reference signal. Similarly, an “uplink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the uplink beam, it is an uplink receive beam, and if a UE is forming the uplink beam, it is an uplink transmit beam.

In 5G, the frequency spectrum in which wireless nodes (e.g., base stations 102/180, UEs 104/182) operate is divided into multiple frequency ranges, FR1 (from 450 to 6000 MHz), FR2 (from 24250 to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In a multi-carrier system, such as 5G, one of the carrier frequencies is referred to as the “primary carrier” or “anchor carrier” or “primary serving cell” or “PCell,” and the remaining carrier frequencies are referred to as “secondary carriers” or “secondary serving cells” or “SCells.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by a UE 104/182 and the cell in which the UE 104/182 either performs the initial radio resource control (RRC) connection establishment procedure or initiates the RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels, and may be a carrier in a licensed frequency (however, this is not always the case).

For example, still referring to FIG. 1 , one of the frequencies utilized by the macro cell base stations 102 may be an anchor carrier (or “PCell”) and other frequencies utilized by the macro cell base stations 102 and/or the mmW base station 180 may be secondary carriers (“SCells”). In carrier aggregation, the base stations 102 and/or the UEs 104 may use spectrum up to Y MHz (e.g., 5, 10, 15, 20, 100 MHz) bandwidth per carrier up to a total of Yx MHz (x component carriers) for transmission in each direction. The component carriers may or may not be adjacent to each other on the frequency spectrum. Allocation of carriers may be asymmetric with respect to the downlink and uplink (e.g., more or less carriers may be allocated for downlink than for uplink). The simultaneous transmission and/or reception of multiple carriers enables the UE 104/182 to significantly increase its data transmission and/or reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically lead to a two-fold increase in data rate (i.e., 40 MHz), compared to that attained by a single 20 MHz carrier.

In order to operate on multiple carrier frequencies, a base station 102 and/or a UE 104 is equipped with multiple receivers and/or transmitters. For example, a UE 104 may have two receivers, “Receiver 1” and “Receiver 2,” where “Receiver 1” is a multi-band receiver that can be tuned to band (i.e., carrier frequency) ‘X’ or band ‘Y,’ and “Receiver 2” is a one-band receiver tuneable to band ‘Z’ only. In this example, if the UE 104 is being served in band ‘X,’ band ‘X’ would be referred to as the PCell or the active carrier frequency, and “Receiver 1” would need to tune from band ‘X’ to band ‘Y’ (an SCell) in order to measure band ‘Y’ (and vice versa). In contrast, whether the UE 104 is being served in band ‘X’ or band ‘Y,’ because of the separate “Receiver 2,” the UE 104 can measure band ‘Z’ without interrupting the service on band ‘X’ or band ‘Y.’

The wireless communications system 100 may further include a UE 164 that may communicate with a macro cell base station 102 over a communication link 120 and/or the mmW base station 180 over an mmW communication link 184. For example, the macro cell base station 102 may support a PCell and one or more SCells for the UE 164 and the mmW base station 180 may support one or more SCells for the UE 164.

The wireless communications system 100 may further include one or more UEs, such as UE 190, that connects indirectly to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks”). In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of the UEs 104 connected to one of the base stations 102 (e.g., through which UE 190 may indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to the WLAN AP 150 (through which UE 190 may indirectly obtain WLAN-based Internet connectivity). In an example, the D2D P2P links 192 and 194 may be supported with any well-known D2D RAT, such as LTE Direct (LTE-D), Wi-Fi Direct (Wi-Fi-D), Bluetooth®, and so on.

According to various aspects, FIG. 2A illustrates an example wireless network structure 200. For example, a 5GC 210 (also referred to as a Next Generation Core (NGC)) can be viewed functionally as control plane functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane functions 212, (e.g., UE gateway function, access to data networks, IP routing, etc.) which operate cooperatively to form the core network. User plane interface (NG-U) 213 and control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210 and specifically to the control plane functions 214 and user plane functions 212. In an additional configuration, an ng-eNB 224 may also be connected to the 5GC 210 via NG-C 215 to the control plane functions 214 and NG-U 213 to user plane functions 212. Further, ng-eNB 224 may directly communicate with gNB 222 via a backhaul connection 223. In some configurations, the New RAN 220 may only have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either gNB 222 or ng-eNB 224 may communicate with UEs 204 (e.g., any of the UEs depicted in FIG. 1 ).

Another optional aspect may include location server 230, which may be in communication with the 5GC 210 to provide location assistance for UEs 204. The location server 230 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The location server 230 can be configured to support one or more location services for UEs 204 that can connect to the location server 230 via the core network, 5GC 210, and/or via the Internet (not illustrated). Further, the location server 230 may be integrated with a component of the core network, or alternatively may be external to the core network. In some examples, the location server 230 can be operated by a carrier or provider of the 5GC 210, a third party, an original equipment manufacturer (OEM), or other party. In some cases, multiple location servers can be provided, such as a location server for the carrier, a location server for an OEM of a particular device, and/or other location servers. In such cases, location assistance data can be received from the location server of the carrier and other assistance data can be received from the location server of the OEM.

According to various aspects, FIG. 2B illustrates another example wireless network structure 250. For example, a 5GC 260 can be viewed functionally as control plane functions, provided by an access and mobility management function (AMF) 264, and user plane functions, provided by a user plane function (UPF) 262, which operate cooperatively to form the core network (i.e., 5GC 260). User plane interface 263 and control plane interface 265 connect the ng-eNB 224 to the 5GC 260 and specifically to UPF 262 and AMF 264, respectively. In an additional configuration, a gNB 222 may also be connected to the 5GC 260 via control plane interface 265 to AMF 264 and user plane interface 263 to UPF 262. Further, ng-eNB 224 may directly communicate with gNB 222 via the backhaul connection 223, with or without gNB direct connectivity to the 5GC 260. In some configurations, the New RAN 220 may only have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either gNB 222 or ng-eNB 224 may communicate with UEs 204 (e.g., any of the UEs depicted in FIG. 1 ). The base stations of the New RAN 220 communicate with the AMF 264 over the N2 interface and with the UPF 262 over the N3 interface.

The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between the UE 204 and a session management function (SMF) 266, transparent proxy services for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and the short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives the intermediate key that was established as a result of the UE 204 authentication process. In the case of authentication based on a UMTS (universal mobile telecommunications system) subscriber identity module (USIM), the AMF 264 retrieves the security material from the AUSF. The functions of the AMF 264 also include security context management (SCM). The SCM receives a key from the SEAF that it uses to derive access-network specific keys. The functionality of the AMF 264 also includes location services management for regulatory services, transport for location services messages between the UE 204 and a location management function (LMF) 270 (which acts as a location server 230), transport for location services messages between the New RAN 220 and the LMF 270, evolved packet system (EPS) bearer identifier allocation for interworking with the EPS, and UE 204 mobility event notification. In addition, the AMF 264 also supports functionalities for non-3GPP access networks.

Functions of the UPF 262 include acting as an anchor point for intra/inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point of interconnect to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) handling for the user plane (e.g., uplink and/or downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic verification (service data flow (SDF) to QoS flow mapping), transport level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding of one or more “end markers” to the source RAN node. The UPF 262 may also support transfer of location services messages over a user plane between the UE 204 and a location server, such as a secure user plane location (SUPL) location platform (SLP) 272.

The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at the UPF 262 to route traffic to the proper destination, control of part of policy enforcement and QoS, and downlink data notification. The interface over which the SMF 266 communicates with the AMF 264 is referred to as the N11 interface.

Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance for UEs 204. The LMF 270 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The LMF 270 can be configured to support one or more location services for UEs 204 that can connect to the LMF 270 via the core network, 5GC 260, and/or via the Internet (not illustrated). The SLP 272 may support similar functions to the LMF 270, but whereas the LMF 270 may communicate with the AMF 264, New RAN 220, and UEs 204 over a control plane (e.g., using interfaces and protocols intended to convey signaling messages and not voice or data), the SLP 272 may communicate with UEs 204 and external clients (not shown in FIG. 2B) over a user plane (e.g., using protocols intended to carry voice and/or data like the transmission control protocol (TCP) and/or IP).

In an aspect, the LMF 270 and/or the SLP 272 may be integrated with a base station, such as the gNB 222 and/or the ng-eNB 224. When integrated with the gNB 222 and/or the ng-eNB 224, the LWF 270 and/or the SLP 272 may be referred to as a “location management component,” or “LMC.” However, as used herein, references to the LMF 270 and the SLP 272 include both the case in which the LMF 270 and the SLP 272 are components of the core network (e.g., 5GC 260) and the case in which the LMF 270 and the SLP 272 are components of a base station.

FIG. 3 shows a block diagram of a design of a base station 102 and a UE 104 that enable transmission and processing of signals exchanged between the UE and the base station, in accordance with some aspects of the present disclosure. Design 300 includes components of a base station 102 and a UE 104, which may be one of the base stations 102 and one of the UEs 104 in FIG. 1 . Base station 102 may be equipped with T antennas 334 a through 334 t, and UE 104 may be equipped with R antennas 352 a through 352 r, where in general T≥1 and R≥1.

At base station 102, a transmit processor 320 may receive data from a data source 312 for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS(s) selected for the UE, and provide data symbols for all UEs. Transmit processor 320 may also process system information (e.g., for semi-static resource partitioning information (SRPI) and/or the like) and control information (e.g., CQI requests, grants, upper layer signaling, and/or the like) and provide overhead symbols and control symbols. Transmit processor 320 may also generate reference symbols for reference signals (e.g., the cell-specific reference signal (CRS)) and synchronization signals (e.g., the primary synchronization signal (PSS) and secondary synchronization signal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO) processor 330 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide T output symbol streams to T modulators (MODs) 332 a through 332 t. The modulators 332 a through 332 t are shown as a combined modulator-demodulator (MOD-DEMOD). In some cases, the modulators and demodulators can be separate components. Each modulator of the modulators 332 a to 332 t may process a respective output symbol stream, e.g., for an orthogonal frequency-division multiplexing (OFDM) scheme and/or the like, to obtain an output sample stream. Each modulator of the modulators 332 a to 332 t may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. T downlink signals may be transmitted from modulators 332 a to 332 t via T antennas 334 a through 334 t, respectively. According to certain aspects described in more detail below, the synchronization signals can be generated with location encoding to convey additional information.

At UE 104, antennas 352 a through 352 r may receive the downlink signals from base station 102 and/or other base stations and may provide received signals to demodulators (DEMODs) 354 a through 354 r, respectively. The demodulators 354 a through 354 r are shown as a combined modulator-demodulator (MOD-DEMOD). In some cases, the modulators and demodulators can be separate components. Each demodulator of the demodulators 354 a through 354 r may condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator of the demodulators 354 a through 354 r may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols. A MIMO detector 356 may obtain received symbols from all R demodulators 354 a through 354 r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor 358 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE 104 to a data sink 360, and provide decoded control information and system information to a controller/processor 380. A channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), and/or the like.

On the uplink, at UE 104, a transmit processor 364 may receive and process data from a data source 362 and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from controller/processor 380. Transmit processor 364 may also generate reference symbols for one or more reference signals (e.g., based at least in part on a beta value or a set of beta values associated with the one or more reference signals). The symbols from transmit processor 364 may be precoded by a TX-MIMO processor 366 if application, further processed by modulators 354 a through 354 r (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to base station 102. At base station 102, the uplink signals from UE 104 and other UEs may be received by antennas 334 a through 334 t, processed by demodulators 332 a through 332 t, detected by a MIMO detector 336 if applicable, and further processed by a receive processor 338 to obtain decoded data and control information sent by UE 104. Receive processor 338 may provide the decoded data to a data sink 339 and the decoded control information to controller (processor) 340. Base station 102 may include communication unit 344 and communicate to a network controller 331 via communication unit 344. Network controller 331 may include communication unit 394, controller/processor 390, and memory 392.

In some aspects, one or more components of UE 104 may be included in a housing. Controller 340 of base station 102, controller/processor 380 of UE 104, and/or any other component(s) of FIG. 3 may perform one or more techniques associated with implicit UCI beta value determination for NR.

Memories 342 and 382 may store data and program codes for the base station 102 and the UE 104, respectively. A scheduler 346 may schedule UEs for data transmission on the downlink and/or uplink.

In some implementations, the UE 104 may include means for determining one or more parameters associated with a communication channel between the UE 104 and the base station 102; means for determining a tone pattern for a reference signal based on the one or more parameters; and means for transmitting the tone pattern to the base station. In some cases, the tone pattern is usable by the base station to send the reference signal to the UE 104.

In some implementations, the base station 102 may include means for determining one or more parameters associated with a communication channel between the UE 104 and the base station 102; means for determining a tone pattern for a reference signal based on the one or more parameters; and means for transmitting the tone pattern to the UE 104. In some cases, the tone pattern is usable by the UE 104 to send the reference signal to the base station 102.

As noted above, tone patterns can be defined as specific arrangement of resource elements in a given resource block for transmission of a reference signal between a UE such as one of UEs 104 and a base station such as base station 102. Tone patterns are currently pre-defined in the 5G communication standard. Pre-defined tone patterns may not be optimized for all environments. In other words, the arrangement or combination of resource elements used for transmission of a particular reference signal may not be optimized across all possible conditions under which a user equipment and a base station operate. Therefore, signal efficiency and latency reduction for 5G mobile systems can be improved through a dynamic determination of optimal tone patterns configurations.

A resource block may be transmitted on UL or DL between the UE 104 and the base station 102 using a radio frame. Various radio frame structures may be used to support downlink and uplink transmissions between network nodes (e.g., base stations and UEs). FIG. 4 is a diagram 400 illustrating an example of a downlink frame structure, according to some aspects of the disclosure. Other wireless communications technologies may have different frame structures and/or different channels.

NR (and LTE) utilizes OFDM on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. Unlike LTE, however, NR has an option to use OFDM on the uplink as well. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, bins, etc. Each subcarrier may be modulated with data. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, and the total number of subcarriers (K) may be dependent on the system bandwidth. For example, the spacing of the subcarriers may be 15 kHz and the minimum resource allocation (resource block) may be 12 subcarriers (or 180 kHz). Consequently, the nominal fast Fourier transform (FFT) size may be equal to 128, 256, 512, 1024, or 2048 for system bandwidth of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), respectively. The system bandwidth may also be partitioned into subbands. For example, a subband may cover 1.08 MHz (i.e., 6 resource blocks), and there may be 1, 2, 4, 8, or 16 subbands for system bandwidth of 1.25, 2.5, 5, 10, or 20 MHz, respectively.

LTE supports a single numerology (subcarrier spacing, symbol length, etc.). In contrast, NR may support multiple numerologies (μ). For example, subcarrier spacing (SCS) of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz or greater may be available. Table 1 provided below lists some various parameters for different NR numerologies.

TABLE 1 Slot Symbol Max. nominal Sym- Slots/ Dura- Dura- system BW SCS bols/ Sub- Slots/ tion tion (MHz) with μ (kHz) Sot frame Frame (ms) (μs) 4K FFT size 0 15 14 1 10 1 66.7 50 1 30 14 2 20 0.5 33.3 100 2 60 14 4 40 0.25 16.7 100 3 120 14 8 80 0.125 8.33 400 4 240 14 16 160 0.0625 4.17 800

In one example, a numerology of 15 kHz is used. Thus, in the time domain, a 10 millisecond (ms) frame is divided into 10 equally sized subframes of 1 ms each, and each subframe includes one time slot. In FIG. 4 , time is represented horizontally (e.g., on the X axis) with time increasing from left to right, while frequency is represented vertically (e.g., on the Y axis) with frequency increasing (or decreasing) from bottom to top.

A resource grid may be used to represent time slots, each time slot including one or more time-concurrent resource blocks (RBs) (also referred to as physical RBs (PRBs)) in the frequency domain. FIG. 4 illustrates an example of a resource block (RB) 402. The resource grid is further divided into multiple resource elements (REs). Referring to FIG. 4 , the RB 402 includes multiple REs, including the resource element (RE) 404. The RE 404 may correspond to one symbol length in the time domain and one subcarrier in the frequency domain. In the numerology of FIG. 4 , for a normal cyclic prefix, RB 402 may contain 12 consecutive subcarriers in the frequency domain and seven consecutive symbols in the time domain, for a total of 84 REs such as RE 404. For an extended cyclic prefix, an RB may contain 12 consecutive subcarriers in the frequency domain and six consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

Some of the REs carry downlink reference (pilot) signals (DL-RS). A DL-RS may include, but is not limited to, PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, etc. FIG. 4 illustrates example locations of REs carrying a DL-RS (with each RE being labeled with an “R”). FIG. 4 and subsequent examples below will be described in relation to CSI-RS and TRS as illustrative examples of DL-RSs. Throughout the disclosure, while references may be made to CSI-RS and/or TRS, it is understood that TRS is a specific type of CSI-RS. However, the present disclosure is not limited thereto and the dynamic based techniques described herein for determining tone patterns can be equally applied to any other DL-RS and/or UL reference (pilot) signals (UL-RS).

A collection of resource elements (REs) that are used for transmission of CSI-RS and/or TRS is referred to as a tone pattern. The tone pattern can span multiple REs over a single RB, multiple REs over multiple RBs in the frequency domain and ‘N’ (e.g., 1 or more) consecutive symbol(s) within a slot in the time domain, or can span a single RE in a given RB.

Locations of REs (labeled “R”) in FIG. 4 represent an example tone pattern for a CSI-RS signal in RB 402 and/or an example tone pattern for a TRS signal. This tone pattern may be defined with REs having (time (subcarrier), frequency (OFDM symbol)) coordinates in the RB 402. For instance, in the example of FIG. 4 the tone pattern can be defined by REs with coordinates (2,4), (4,4), (6,4) and (8,4) in the RB 402. These coordinates define the locations of the REs labeled “R” in FIG. 4 . Various other tone patterns are defined for CSI-RS and TRS for the 5G standard by the 3GPP. The tone pattern shown in FIG. 4 as well as other tone patterns specified by the 3GPP, are pre-defined. In other words, when a CSI-RS or TRS is to be sent on a DL channel from base station 102 to UE 104 (which can be in every RB, every second RB, etc.), one such pre-defined tone pattern is used by the base station 102 to transmit the CSI-RS and/or TRS to the UE 104.

Relying on pre-defined tone patterns decoupled from conditions under which a UE and a base station communicate (channel conditions), may result in redundant and/or otherwise inefficient use of network resources for sending CSI-RS and/or TRS from the base station to the UE. One example of such inefficient use of network resources includes the overhead associated with transmission of reference signals, which can be reduced by implementing techniques described below for determining tone patterns for transmission of reference signals.

As noted above, systems and techniques are described herein for a process of determining tone patterns (can also be referred to as customized and/or optimized tone patterns) for transmission of references signals between UEs and base stations. In some cases, the systems and techniques can be implemented by a base station, such as the base station 102. In some examples, the systems and techniques can be implemented by a UE, such as the UE 104. The systems and techniques can determine and communicate a customized tone pattern based on current and/or prior conditions of a communication channel between a UE and a base station. The systems and techniques described herein can also utilize a trained machine learning model for determining a customized and/or optimized tone pattern for reference signal transmissions between UEs and base stations.

Configuring a UE and/or a base station to determine an optimized (and/or customized) tone pattern (e.g., each time CSI-RS and/or TRS is to be sent on the DL channel to the UE) can improve the efficiency of network resource utilization at the physical layer, can reduce overhead associated with transmission of reference signals (e.g., based on Energy Per Resource Element (EPRE)=0 for some RS tones, as described herein), etc.

For example, under certain conditions, such as UE mobility conditions, environmental conditions, and/or transmission and reception capabilities of UEs and base stations (e.g., a UE having an advanced receiver that can estimate channels using lower RS tones, etc.), use of the example tone pattern in FIG. 4 may be redundant or inefficient. In such cases and in other cases, sending the CSI-RS or the TRS over four different REs may be redundant. Using the systems and techniques described herein, under such conditions or others, the UE and/or base station can customize the tone pattern for transmission of reference signals. As a non-limiting example, sending the CSI-RS or the TRS using one or two REs instead of four tones may suffice.

In another example, environmental conditions may be such that signal reception at the UE 104 is sub-optimal (less than a threshold). In this instance, the UE 104 may send the CSI-RS or the TRS over more than four REs. In yet another example, and depending on channel and device configurations, certain REs in a given RB may be reserved for communication of other pilot signals, which would intervene with REs to be used for transmission of CSI-RS and/or TRS signals.

While a number of example instances are described above under which use of a pre-defined tone pattern may be sub-optimal, there may be various other conditions in which the use of customized or alternative tone patterns compared to pre-defined tone patterns defined in the standard can be advantageous. A custom tone pattern may be determined by generating a new tone pattern based on channel conditions. In some cases, the new tone pattern is not defined in the 3GPP Standard. For instance, the new tone pattern can include a customized (e.g., irregular) placement of tones in a resource block. Illustrative examples of such new tone patterns are described below with respect to FIG. 8A and FIG. 8B. In either case, whether determining a new tone pattern or using an existing pattern, making a determination of the best tone pattern to use for transmission of CSI-RS or TRS signals, can improve the spectral efficiency and reduce latency.

In some examples, the tone pattern optimization systems and techniques described herein can take into account channel conditions or other factors when determining optimized tone patterns. For instance, UEs (e.g., the UEs 104 described above with reference to FIG. 1 ) receive CSI-RSs and/or TRSs on DL channels from base station(s) (e.g., one or more of the base stations 102 described above with reference to FIG. 1 and to which the UEs are connected). The CSI-RS may be used by a UE to make one or more measurements including, but not limited to, channel throughput, Reference Signal Receive Power (RSRP) during mobility and beam management, frequency and/or time tracking, demodulation and UL reciprocity based pre-coding, etc. Similarly, a TRS can be used by a UE to make one or more measurements including, but not limited to, time and frequency tracking, path delay spread and Doppler spread, etc.

Such measurements made by UEs may vary depending on channel conditions (e.g., environment conditions, mobility status, etc.) and the specific tone pattern over which CSI-RS and/or TRS are sent to the UEs by their respective base stations. Accordingly, UEs can determine over time, which tone patterns result in better measurements for a given set of channel conditions.

In some examples, a machine learning model may be used to determine optimized tone patterns and/or EPRE values. For instance, as described in more detail below, a machine learning model may be trained using a training dataset. In some aspects, the training dataset can include channel conditions, different tone patterns over which UEs receive CSI-RS and TRS signals, resulting measurements made by the receiving UEs under such channel conditions and tone patterns, measured throughput, etc. Once trained, the machine learning model can receive as input channel conditions (e.g., one or more parameters associated with a communication channel between the UE 104 and the base station 102 to which it is connected such as mobility status of the UE 104, environmental conditions in which the UE 104 is operating, etc.) and provide as output a recommended tone pattern for the base station 102 to use for sending CSI-RS and/or TRS on the DL channel to the UE 104.

Examples of a tone pattern optimization process will now be described with reference to FIGS. 5-10 .

FIG. 5 illustrates an example neural architecture of a neural network 500 that can be trained for tone placement optimization, in accordance with some aspects of the present disclosure. The example neural architecture of the neural network 500 may be defined by an example neural network description 502 in neural controller 501. The neural network 500 is an example of a machine learning model that can be deployed and implemented at the base station 102 and/or the UE 104. The neural network 500 can be a feedforward neural network or any other known or to-be-developed neural network or machine learning model.

The neural network description 502 can include a full specification of the neural network 500, including the neural architecture shown in FIG. 5 . For example, the neural network description 502 can include a description or specification of architecture of the neural network 500 (e.g., the layers, layer interconnections, number of nodes in each layer, etc.); an input and output description which indicates how the input and output are formed or processed; an indication of the activation functions in the neural network, the operations or filters in the neural network, etc.; neural network parameters such as weights, biases, etc.; and so forth.

The neural network 500 can reflect the neural architecture defined in the neural network description 502. In this non-limiting example, the neural network 500 includes an input layer 503, which can receive one or more sets of input data. The input data can be any type of data such as one or more parameters associated with a communication channel (e.g., DL communication channel between the UE 104 and the base station 102 such as environmental conditions, UE mobility status, etc.), various measurements made by the UE 104 using previously transmitted CSI-RS or TRS (e.g., tone patterns used in previous RBs for transmission of CSI-RS or TRS, etc.), etc.

The neural network 500 can include the hidden layers 504A through 504N (collectively “504” hereinafter). The hidden layers 504 can include n number of hidden layers, where n is an integer greater than or equal to one. The number of hidden layers can include as many layers as needed for a desired processing outcome and/or rendering intent. In one illustrative example, any one of the hidden layer 504 can include data representing one or more of the data provided at the input layer 503 such as one or more parameters associated with a communication channel (e.g., DL communication channel between the UE 104 and the base station 102 such as environmental conditions, UE mobility status, etc.), previously utilized tone patterns for communication of CSI-RS and/or TRS signals by the base station 102 to the UE 104, one or more measurements and associated throughputs made by the UE 104 using the previously utilized tone patterns, a delta (difference) between measurements made by the UE 104 using the different tone patterns, etc.

The neural network 500 further includes an output layer 506 that provides an output resulting from the processing performed by hidden layers 504. In one illustrative example, the output layer 506 can provide output data based on the input data. In one example context related to determination of a tone pattern for transmission of CSI-RS and/or TRS, the output data can include a recommended tone pattern to be used by the base station 102 for sending a CSI-RS and/or a TRS on the DL channel to the UE 104.

The neural network 500, in this example, is a multi-layer neural network of interconnected nodes. Each node can represent a piece of information. Information associated with the nodes is shared among the different layers and each layer retains information as information is processed. In some cases, the neural network 500 can include a feed-forward neural network. In other cases, the neural network 500 can include a recurrent neural network, which can have loops that allow information to be carried across nodes while reading in input.

Information can be exchanged between nodes through node-to-node interconnections between the various layers. The nodes of the input layer 503 can activate a set of nodes in the first hidden layer 504A. For example, as shown, each input node of the input layer 503 is connected to each node of the first hidden layer 504A. The nodes of the hidden layer 504A can transform the information of each input node by applying activation functions to the information. The information derived from the transformation can then be passed to and can activate the nodes of the next hidden layer (e.g., 504B), which can perform their own designated functions. Example functions include convolutional, up-sampling, data transformation, pooling, and/or any other suitable functions. The output of hidden layer (e.g., 504B) can then activate nodes of the next hidden layer (e.g., 504/V), and so on. The output of last hidden layer can activate one or more nodes of the output layer 506, at which point an output is provided. In some cases, while nodes (e.g., nodes 508A, 508B, 508C) in the neural network 500 are shown as having multiple output lines, a node has a single output and all lines shown as being output from a node represent the same output value.

In some cases, each node or interconnection between nodes can have a weight that is a set of parameters derived from training the neural network 500. For example, an interconnection between nodes can represent a piece of information learned about the interconnected nodes. The interconnection can have a numeric weight that can be tuned (e.g., based on a training dataset), allowing the neural network 500 to be adaptive to inputs and able to learn as more data is processed.

The neural network 500 can be pre-trained to process the features from the data in the input layer 503 using different hidden layers 504 in order to provide the output through the output layer 506.

In some cases, the neural network 500 can adjust weights of nodes using a training process called backpropagation. Backpropagation can include a forward pass, a loss function, a backward pass, and a weight update. The forward pass, loss function, backward pass, and parameter update can be performed for one training iteration. The process can be repeated for a certain number of iterations for each set of training data until the weights of the layers are accurately tuned (e.g., meet a configurable threshold determined based on experiments and/or empirical studies).

The neural network 500 can include any suitable neural or deep learning type of network. One example includes a convolutional neural network (CNN), which includes an input layer and an output layer, with multiple hidden layers between the input and out layers. The hidden layers of a CNN include a series of convolutional, nonlinear, pooling (for downsampling), and fully connected layers. In other examples, the neural network 500 can represent any other neural or deep learning network, such as an autoencoder, a deep belief nets (DBNs), a recurrent neural network (RNN), etc.

Once trained, the neural network 500 can receive as input one or more parameters associated with a communication channel between the base station 102 and the UE 104. Such parameters can include, but are not limited to, environmental conditions in which the base station 102 and the UE 104 are communicating (e.g., weather conditions, indoor/outdoor channel conditions, and/or cellular or wireless connectivity, transmission capabilities and power of the base station 102 and/or the UE 104, etc.) In some aspects, environmental conditions may further include a mobility status of the UE 104 (e.g., how fast the UE 104 is moving toward or away from the base station 102, etc.), multipath characteristics of the channel, and/or various measurements made by the UE 104 using previously transmitted CSI-RS or TRS (e.g., tone patterns used in previous RB s for transmission of CSI-RS or TRS, etc.). Based on the environmental conditions, an optimal tone pattern configuration can be determined and provided, e.g., as an output recommendation for a tone pattern to be used for transmission of the next CSI-RS or TRS from the base station 102 to the UE 104.

In some examples, a receiver (e.g., the base station 102 and/or the UE 104) can monitor, in real-time (e.g., as REs are received) or in near-real-time, received REs over time and/or frequency and may determine over a period of time that certain REs are better for the purpose of transmission of reference signals (RSs). In some cases, even tone patterns used for data transmission can be interpreted as pilots after they are decoded. The receiver may also determine the optimized power distribution across the tone patterns over which reference signals are received. Once an optimized power distribution is determined, the receiver can then feed the optimized pattern back to a transmitter to be used for transmission of reference signals. Depending on the desired implementation, the transmitter may be associated with the same device as the receiver or may be associated with a different device, e.g., another base station 102 and/or UE 104.

Trained neural network 500 can log parameters, channel conditions, and/or other information (e.g., UE position, etc.), and can associate the same with various tone patterns used for reference signal transmission. As noted herein, the channel conditions can include indoor and/or outdoor channel conditions, UE mobility status, multipath channel characteristics, and/or other channel conditions, or any combination thereof. For example, the trained neural network 500 can remember which tone patterns were used for the logged information, so that they can be used in the future. Based on subsequent measurements and channel estimation processes, the trained neural network 500 can identify which tone patterns are best suited for a given set of parameters and channel conditions. In the future and upon occurrence or detection of such channel conditions, the trained neural network 500 is capable of identifying and using the best suited tone pattern(s) under the detected channel conditions for transmission of reference signals. In some examples, the trained neural network 500 may be continuously updated or retrained (e.g., using an online learning approach). For example, trained neural network may be configured to be optimized each time reference signals are transmitted between the base station 102 and the UE 104 under various conditions.

As will be described below, the trained neural network 500 can be deployed at the UE 104 or alternatively at the base station 102. In one example, when the machine learning model is deployed at the base station 102, the base station 102 may receive the one or more input parameters from the UE 104 and determine the best tone pattern for the CSI-RS or TRS as output of the trained neural network.

As noted above, the output of the trained neural network 500, whether implemented at the base station 102 or the UE 104, can be a customized and new tone pattern not previously utilized for transmission of CSI-RS or TRS signal. Alternatively, the output of the trained neural network 500 can be a previously utilized tone pattern that the trained neural network 500 determines to be the best tone pattern for the given set of one or more parameters provided as input.

FIG. 6 is a flow chart of a process 600 of training a machine learning algorithm, such as neural network 500, for tone pattern determination, in accordance with some aspects of the present disclosure. Operation of FIG. 6 will be described in relation to FIG. 5 . Neural network 500 may be implemented at the base station 102 or the UE 104.

At operation 610, the neural controller 501 receives a description of the structure of the neural network 500 (e.g., from base station 102) including, but not limited to, the architecture of the neural network 500 and definition of layers, layer interconnections, input and output descriptions, activation functions, operations, filters, parameters such as weights, coefficients, biases, etc. In some examples, the description can be received from a device based on a user input received by the device (e.g., input via an input device, such as a keyboard, mouse, touchscreen interface, and/or other type of input device). In some examples, operation 610 is optional and may not be performed. For example, the neural network 500 can be UE specific (e.g., executed by the UE) and thus the description and specific configurations of the neural network 500 may be provided by the UE 104. At operation 620, the neural network 500 is generated based on the description received at operation 610. Using the description, the neural controller 501 generates appropriate input, intermediate, and output layers with defined interconnections between the layers and/or any weights or coefficients assigned thereto. The weights and/or other coefficients can be set to initialized values, which will be modified during training, as described below. In some examples, operation 620 is optional and may not be performed (e.g., when the neural network 500 is UE specific).

At operation 630, once the neural network 500 is defined, a training data set is provided to the input layer 503 of the neural network 500. As described above, the training data set can include, but is not limited to, various tone patterns used for transmission of reference signals between the base station 102 and the UE 104, one or more parameters associated with a communication channel (e.g., DL communication channel between the UE 104 and the base station 102) such as weather conditions, cellular or wireless connectivity, transmission capabilities and power of the base station 102 and/or the UE 104, environmental conditions, UE mobility status, indoor/outdoor conditions, multipath characteristics of the communication channel, etc. Furthermore, previously utilized tone patterns for communications from the base station 102 to the UE 104, one or more measurements and associated throughputs made by the UE 104 using the previously utilized tone patterns, a difference between measurements made by the UE 104 using the different tone patterns, etc., may be utilized in training the neural network 500. In some examples, there may not be an explicitly dedicated training data set for the purpose of training the neural network 500 or the training data set may not necessarily be a predetermined set of conditions and associated tone patterns. For instance, in some cases, the neural network 500 may instead be trained using information associated with conditions under which the base station 102 and the UE 104 are communicating and transmitting reference signals using tone patterns. In such examples, the real-time data can be used for live training of the neural network 500, for example, using an online-learning approach.

At operation 640, the neural network 500 is trained using the training data set. In one example, the training of the neural network 500 is an iterative process repeated multiple times and each time validated against a test data set. The test data set may include a set of one or more parameters similar to those used as part of the training dataset and associated output tone patterns. During each iteration, the output at the output layer 506 is compared to the test data set and a delta between the output at the output layer 506 at that iteration and the optimized output defined in the test data set is determined. The weights and other coefficients of the various layers can be adjusted based on the delta. The iterative process may continue until the delta for any given set of input parameters is less than a threshold. The threshold may be a configurable parameter determined based on experiments and/or empirical studies.

At operation 650 and once the neural network 500 is trained, the trained neural network 500 is deployed at the base station 102 and/or the UE 104. The trained neural network, as will be described below, can then be used to determine tone patterns, e.g., for a CSI-RS or a TRS, given a set of input parameters associated with the communication channel between the base station 102 and the UE 104. The periodicity of the tone pattern determination can depend on any number of factors including, but not limited to a configured periodicity for transmission of reference signals, e.g., every subframe or RB, every other subframe or RB, every frame, etc. As the channel condition or other parameters change, the receiving device (e.g., the base station 102 or the UE 104 on which the trained neural network 500 is deployed) can re-train the neural network 500 to determine optimized tone pattern and/or channel conditions for the new conditions.

At operation 660, a triggering condition for retraining the neural network 500 is detected. The command may be received after the trained neural network 500 is deployed and after each instance of determining a tone pattern for CSI-RS or TRS. In other words, each time the trained neural network 500 determines a tone pattern for CSI-RS and TRS transmission, the corresponding parameters used as input and the tone pattern are used to retrain and optimize the trained neural network 500. The corresponding parameters used as input and the tone pattern are provided as part of the command received for retraining the neural network 500. In another example, the command may be received upon a detection of a triggering condition, which will be further described below with reference to FIGS. 7 and 9 . Examples of such triggering conditions can further include, but are not limited to, a threshold degradation in performance of tone patterns recommended by the neural network 500 for transmission of reference signal (where the threshold may be determined based on experiments and/or empirical studies), channel estimation errors when a reference signal is used for channel estimation (e.g., when channel estimation error reaches and/or exceeds a configurable threshold more than a number of times over a period of time, with the number of times and the period of time being configurable parameters determined based on experiments and/or empirical studies), etc.

At operation 670, the neural network 500 is retrained using the corresponding parameters used as input and the tone pattern received as part of the command at operation 660. Retraining the neural network 500 may include utilizing the input and the tone pattern to adjust weights, coefficients and/or biases at different nodes of the different layers of the neural network 500. The operation 660 and 670 (the retraining of the neural network 500) may be continuously repeated, thus resulting in increased accuracy of the neural network 500 over time.

FIG. 7 is a flow chart of a process 700 of communicating a customized tone pattern, in accordance with some aspects of the present disclosure. The process 700 of FIG. 7 will be described from the perspective of the UE 104. It should be understood that the UE 104 may have one or more processors configured to execute one or more computer-readable instructions stored on one or more associated memories of the UE 104 to implement the steps of FIG. 7 . In describing operations of FIG. 7 , the UE 104 may be a receiving device and the base station 102 may be a transmitting device.

At operation 710, the UE 104 determines one or more parameters associated with communications between the base station 102 and the UE 104 (and/or parameters associated between the UE 104 and other devices, such as other base stations and/or other UEs). The communications between the base station 102 and the UE 104 can be over a communication channel used over a duration of time (e.g., current communication channel between the base station 102 and the UE 104). As described above, the one or more parameters can include, but are not limited to, a location of the UE 104 (e.g., relative to the location of the base station 102), an environmental condition associated with the location of the UE 104, a location of the base station 102, an environmental condition associated with the location of the base station 102, indoor/outdoor channel conditions, multipath characteristics of the channel, a mobility status of the UE 104 (e.g., how fast the UE 104 is moving toward or away from the base station 102, etc.), various measurements made by the UE 104 using previously transmitted CSI-RS or TRS (e.g., tone patterns used in previous RB s for transmission of CSI-RS or TRS, etc.) and provided as output a recommendation for a tone pattern to be used for transmission of the next CSI-RS (e.g., a TRS) by the base station 102 to the UE 104, any combination thereof, and/or other parameters. The one or more parameters may be measured by the UE 104 over a period of time (e.g., 1 ms, 5 ms, 10 s, one minute, etc.).

At operation 720, the UE 104 determines a tone pattern for a reference signal (e.g., a CSI-RS, a TRS, etc.) for use in future communication(s) between the base station 102 and the UE 104. In one example, the UE 104 determines the tone pattern based on the one or more parameters determined at operation 710. The tone pattern may identify one or more symbols and one or more locations within a resource block for the one or more symbols to be placed. In one example, the UE 104 may determine the tone pattern using a trained machine learning model (e.g., the trained neural network 500) of FIG. 5 . In another example, one or more signal processing techniques may be applied to determine the tone pattern. For example, the UE 104 can have advanced receiver capabilities built therein allowing the UE 104 to use fewer tones for a reference signal (which can be used for channel estimation).

As described above, the trained machine learning model can determine the tone pattern based on the one or more parameters determined at operation 710. In some cases, the UE 104 may have a number of machine learning models, each of which may be tuned/trained for determining optimized tone patterns under a given condition corresponding to the one or more parameters (e.g., a location of the UE 104, an environmental condition associated with the location of the UE 104, a location of the base station 102, an environmental condition associated with the location of the base station 102, indoor/outdoor channel conditions, multipath characteristics of the channel, a mobility status of the UE 104, etc.). In such cases, the UE 104 may determine which tuned/trained machine learning model to use based on the one or more parameters. In some cases, the one or more parameters may be provided as input into a machine learning model that has been tuned/trained over time to identify optimized tone patterns for any given set of parameters (e.g., the one or more parameters). In such cases, the machine learning model can run inference on the one or more parameters to determine an optimized tone pattern based on the one or more parameters.

In some examples, the tone pattern can be for a DL reference signal (e.g., a CSI-RS or other DL reference signal) to be transmitted by the base station 102 to the UE 104. For example, the tone pattern can be a new tone pattern, such as a customized irregular arrangement of REs with different (time, frequency) coordinates. FIGS. 8A-B illustrate non-limiting examples of customized irregular tone pattern arrangements, in accordance with some aspects of the present disclosure. FIG. 8A is an example of a configuration 800 of the RB 402 of FIG. 4 having resource elements (REs) 404, as described above with reference to FIG. 4 . The configuration 800 in FIG. 8A is a customized irregular arrangement of REs for a tone pattern, which may also be referred to as an irregular combination of REs, including an RE 802, an RE 804, and an RE 806. In contrast, the arrangement of the tones in FIG. 4 are pre-defined and regular, as defined by the 3GPP Standard. The REs 802, 804, and 806 shown in FIG. 8A are placed across the RB 402 without any set pattern (repetition or periodicity) to their placement within the RB 402. In the example configuration 800, the REs 802, 804, and 806 have (time, frequency) coordinates (1,12), (2,4) and (4,9) and define a non-limiting example of a customized irregular tone pattern.

FIG. 8B is another example of a customized irregular tone pattern arrangement shown as configuration 850. In this example, a tone pattern of the configuration 850 is defined by two clusters of REs, including cluster 852 and cluster 860. Cluster 852 includes an RE 854, an RE 856, and an RE 858, while cluster 860 includes an RE 862, an RE 864, an RE 866, and an RE 868. Each cluster may be defined by REs that are within a threshold location of one another in the RB 402 in time and/or in frequency. For example, the REs 854, 856, and 858 of the cluster 852 are separated by at most one subcarrier (time) and/or one OFDM symbol (frequency). Accordingly, the REs 854, 856, and 858 are within the threshold of two OFDM symbols and two subcarriers of each other. In another example, the REs 862, 864, 866, and 868 of the cluster 860 are separated by at most one subcarrier (time) and/or two OFDM symbols (frequency). Accordingly, the REs 862, 864, 866, and 868 are within the threshold of three OFDM symbols and two subcarriers of each other. Accordingly, the thresholds in time and frequency may not be the same and may be different as shown with reference to the example clusters 852 and 860 of FIG. 8B. The clusters of REs, such as the clusters 852 and 860, may also be referred to as clumps of REs. While FIG. 8B illustrates two example clusters as forming an example tone pattern (e.g., for CSI-RS, such as a TRS), the present disclosure is not limited thereto and a tone pattern may be formed of a single cluster or more than two clusters.

In some cases, the tone pattern can be a regular tone pattern (e.g., with a defined and repeatable arrangement of REs), which has not been defined by the 3GPP standard. In some examples, the tone pattern determined at operation 720 can be one of a plurality of existing tone patterns defined by the 3GPP standard. An example of such existing tone pattern is shown in FIG. 4 (i.e., the REs of the RB 402 designated with an “R”). The existing tone pattern of FIG. 4 is an example of a pre-defined regular arrangement of REs for the tone pattern shown in FIG. 4 .

At operation 730, the UE 104 can transmit (send) the tone pattern to the base station 102. The base station 102 can determine whether to utilize the tone pattern for transmission of a CSI-RS (e.g., a TRS) on the downlink channel to the UE 104. At operation 740, the UE 104 may receive, via one or more transceivers of the UE 104, the CSI-RS (e.g., the TRS) from the base station 102 on a DL channel. At operation 750, the UE 104 can perform one or more measurements based on the CSI-RS or the TRS including, but not limited to, measuring channel throughput, measuring Reference Signal Receive Power (RSRP) during mobility and beam management, frequency/time tracking, demodulation and UL reciprocity based pre-coding, etc. In some cases, the CSI-RS (e.g., the TRS) can be used by the UE 104 to determine one or more measurements including, but not limited to, time and frequency tracking, path delay spread, Doppler spread, etc.

At operation 760, the UE 104 can determine if a triggering condition for retraining the neural network 500 has occurred. In one example, the triggering condition can be a change in the mobility status of the UE 104. In another example, the triggering condition can be the determination of the tone pattern at operation 720. In some cases, the triggering condition can be the performance of operation 720. Examples of such triggering conditions can further include, but are not limited to, a threshold degradation in performance of tone patterns recommended by the neural network 500 for transmission of reference signal (where the threshold may be determined based on experiments and/or empirical studies), channel estimation errors when a reference signal is used for channel estimation (e.g., when channel estimation error reaches and/or exceeds a configurable threshold more than a number of times over a period of time, with the number of times and the period of time being configurable parameters determined based on experiments and/or empirical studies), any combination thereof, and/or other triggering conditions.

If at operation 760, the UE 104 determines that a triggering condition has not occurred, the process 700 reverts back to operation 710 and operations 710 through 760 may be periodically repeated, for example, depending on the frequency of transmission of a CSI-RS (e.g., a TRS) to the UE 104 by the base station 102 (e.g., every subframe (1 ms), every other subframe, etc.) for determination of a tone pattern for the CSI-RS (e.g., for a TRS transmission). However, if at operation 760, the UE 104 determines that a triggering condition has occurred, at operation 770, the UE 104 can retrain the neural network 500 per operations 660 and 670 of FIG. 6 described above. For instance, the one or more parameters determined at operation 710 and/or the tone pattern determined at operation 720 may be provided as input to the input layer 503 of the neural network 500 for retraining the neural network 500. In one example, the retraining may involve adjusting coefficients, biases, and/or weights of different nodes (e.g., nodes 508A, 508B, 508C) at different network layers of the neural network 500. Thereafter, the process 700 may revert back to operation 710 and the UE 104 may perform the process of FIG. 7 periodically depending on the frequency of transmission of CSI-RS or TRS to the UE 104 by the base station 102 (e.g., every subframe (1 ms), every other subframe, etc.).

As noted above, the process of determining a tone pattern using the trained neural network 500 of FIG. 5 may be performed at the base station 102. FIG. 9 is a flow chart of an example process 900 of communicating a customized tone pattern, in accordance with some aspects of the present disclosure. The process 900 of FIG. 9 will be described from the perspective of the base station 102. It should be understood that the base station 102 may have one or more processors configured to execute one or more computer-readable instructions stored on one or more associated memories of the base station 102 to implement the steps of FIG. 9 . In describing operations of FIG. 9 , the base station 102 may be a receiving device and the UE 104 may be a transmitting device.

At operation 910, the base station 102 determines one or more parameters associated with communications between the base station 102 and the UE 104. Such communications between the base station 102 and the UE 104 can be over a communication channel used over a duration of time (e.g., current communication channel between the base station 102 and the UE 104). In one example, the base station 102 determines the one or more parameters by receiving the parameters from the UE 104. The base station 102 can receive the one or more parameters from the UE 104 on one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer. The one or more parameters may be measured by the UE 104 over a period of time (e.g., 1 ms, 5 ms, 10 s, one minute, etc.).

As described above, the one or more parameters include but are not limited to, a location of the UE 104 (e.g., relative to the location of the base station 102), an environmental condition associated with the location of the UE 104, a location of the base station 102, an environmental condition associated with the location of the base station 102, indoor/outdoor channel conditions, multipath characteristics of the channel, a mobility status of the UE 104 (e.g., how fast the UE 104 is moving toward or away from the base station 102, etc.), various measurements made by the UE 104 using a previously transmitted CSI-RS or TRS (e.g., tone patterns used in previous RBs for transmission of CSI-RS or TRS, etc.) and provided as output a recommendation for a tone pattern to be used for transmission of the next CSI-RS or TRS by the base station 102 to the UE 104, any combination thereof, and/or other parameters.

At operation 920, the base station 102 determines a tone pattern for a reference signal (e.g., a CSI-RS, a TRS, etc.) for use in future communication(s) between the base station 102 and the UE 104. In one example, the base station 102 determines the tone pattern based on the one or more parameters determined at operation 910. The tone pattern may identify one or more symbols and one or more locations within a resource block for the one or more symbols to be placed. In one example, the base station 102 may determine the tone pattern using a trained machine learning model (e.g., the trained neural network 500) of FIG. 5 . As described above, the trained machine learning model can receive as input the one or more parameters determined at operation 910 and provide as output the tone pattern at operation 920. In another example, one or more signal processing techniques may be applied to determine the tone pattern. For example, the UE 104 can have advanced receiver capabilities built therein, allowing the UE 104 to use fewer tones for a reference signal (which, for example, can be used for channel estimation). In another example, the base station 102 may determine the tone pattern by receiving the tone pattern from the UE 104.

In some examples, the tone pattern can be for a DL reference signal (e.g., a CSI-RS or other DL reference signal) to be transmitted by the base station 102 to the UE 104. In some examples, the tone pattern can be a new pre-defined regular tone pattern, which has not been defined by the 3GPP standard. In another example, the tone pattern can be a new customized irregular arrangement of REs with different (time, frequency) coordinates. Two non-limiting examples of customized irregular tone pattern arrangements have been described above with reference to FIGS. 8A and B. In some examples, the tone pattern determined at operation 910 can be one of a plurality of existing tone patterns defined by the 3GPP standard. An example of such an existing tone pattern is shown in FIG. 4 (i.e., the REs of the RB 402 designated with an “R”). The existing tone pattern of FIG. 4 is an example of a pre-defined regular arrangement of REs for the tone pattern shown in FIG. 4 .

At operation 930, the base station 102 transmits (or sends) the CSI-RS or TRS to the UE 104 using the tone pattern determined at operation 920 on a DL channel. As noted above, the UE 104 can detect and decode the CSI-RS or TRS and perform one or more measurements based on the CSI-RS or the TRS including, but not limited to, measuring channel throughput, measuring Reference Signal Receive Power (RSRP) during mobility and beam management, frequency/time tracking, demodulation and UL reciprocity based pre-coding, etc. Similarly, TRS can be used by a receiving UE to make one or more measurements including, but not limited to, time and frequency tracking, path delay spread and Doppler spread, etc.

At operation 940, the UE 104 can determine if a triggering condition for retraining the neural network 500 has occurred. In one example, the triggering condition can be a change in the mobility status of the UE 104. Such triggering condition may be communicated to the base station 102 by the UE 104 over an UL channel. In another example, the triggering condition can be the determination of the tone pattern at operation 920. In some cases, the triggering condition can be the performance of operation 920. Examples of such triggering conditions can further include, but are not limited to, a threshold degradation in performance of tone patterns recommended by the neural network 500 for transmission of reference signal(s) (where the threshold may be determined based on experiments and/or empirical studies), channel estimation errors when a reference signal is used for channel estimation (e.g., when channel estimation error reaches and/or exceeds a configurable threshold more than a number of times over a period of time, with the number of times and the period of time being configurable parameters determined based on experiments and/or empirical studies), any combination thereof, and/or other triggering conditions.

If, at operation 940, the base station 102 determines that a triggering condition has not occurred, the process 900 reverts back to operation 910 and operations 910 through 940 may be periodically repeated (depending on the frequency of transmission of CSI-RS or TRS to the UE 104 by the base station 102 (e.g., every subframe (1 ms), every other subframe, etc.) for determination of a tone pattern for CSI-RS or TRS transmission. However, if at operation 940, the base station 102 determines that triggering condition has occurred, at operation 950, the UE 104 can retrain the neural network 500 per operations 660 and 670 of FIG. 6 described above. For instance, the one or more parameters determined at operation 710 and/or the tone pattern determined at operation 720 may be provided as input to the input layer 503 of the neural network 500 for retraining the neural network 500. In one example, the retraining may involve adjusting coefficients, biases, and/or weights of different nodes (e.g., nodes 508A, 508B, 508C) at different network layers of the neural network 500. Thereafter, the process 900 may revert back to operation 910 and the base station 102 may perform the process of FIG. 9 periodically depending on the frequency of transmission of CSI-RS or TRS to the UE 104 by the base station 102 (e.g., every subframe (1 ms), every other subframe, etc.).

FIG. 10 is a flow chart of an example process 1000 of communicating a customized tone pattern, in accordance with some aspects of the present disclosure. The process 1000 of FIG. 10 is described from the perspective of a user equipment, which can be the same as the UE 104 in some examples. At operation 1010, the process 1000 includes determining, by a user equipment, one or more parameters associated with communications with a base station over a time duration. In some cases, the one or more parameters include at least a location of the user equipment, an environmental condition associated with the location, a mobility status of the user equipment, any combination thereof, and/or other parameters.

At operation 1020, the process 1000 includes determining, by the user equipment and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration. In some aspects, the tone pattern is a customized irregular arrangement of resource elements in a resource block for communication of the reference signal. In some cases, the tone pattern is determined from a plurality of existing tone patterns for communication of the reference signal. In some cases, the tone pattern is determined using a machine learning model, such as the neural network 500 of FIG. 5 . In some aspects, the machine learning model receives as input the one or more parameters and provides as output the tone pattern. In some examples, the user equipment can determine a machine learning model to use (e.g., from a plurality of previously trained/tuned models) based on the one or more parameters, as described above. In some cases, the machine learning model can be retrained upon detection of a triggering condition. In some cases, the triggering condition is at least one of a change in a mobility status of the user equipment and a channel estimation error, as described above. In some cases, the reference signal can be any one of Channel State Information—Reference Signal (CSI-RS), a Tracking Reference Signal (TRS) or any other downlink reference signal.

At operation 1030, the process 1000 includes transmitting the tone pattern to the base station. In some cases, transmitting the tone pattern can include transmitting one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed. In some cases, the tone pattern can be transmitted to the base station over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer. In some cases, and upon transmission of the tone pattern to the base station, the user equipment can receive, using a transceiver and from the base station, the reference signal having the tone pattern (e.g., over resource elements corresponding to the tone pattern).

FIG. 11 is a flow chart of an example process 1100 of communicating a customized tone pattern, in accordance with some aspects of the present disclosure. The process 1100 of FIG. 11 is described from the perspective of a base station, which can be the same as the base station 102 in some examples. At operation 1110, the process 1100 includes determining, by a base station, one or more parameters associated with communications with a user equipment over a time duration. In some cases, the one or more parameters include at least a location of the user equipment, an environmental condition associated with the location, a mobility status of the user equipment, any combination thereof, and/or other parameters.

At operation 1120, the process 1100 includes determining, by the base station and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user equipment over a future time duration. In some aspects, the tone pattern is a customized irregular arrangement of resource elements in a resource block for communication of the reference signal. In some cases, the tone pattern is determined from a plurality of existing tone patterns for communication of the reference signal. In some cases, the tone pattern is determined using a machine learning model, such as the neural network 500 of FIG. 5 . In some aspects, the machine learning model receives as input the one or more parameters and provides as output the tone pattern. In some examples, the base station can determine a machine learning model to use (e.g., from a plurality of previously trained/tuned models) based on the one or more parameters, as described above. In some cases, the machine learning model can be retrained upon detection of a triggering condition. In some cases, the triggering condition is at least one of a change in a mobility status of the user equipment and a channel estimation error, as described above. In some cases, the reference signal can be any one of Channel State Information—Reference Signal (CSI-RS), a Tracking Reference Signal (TRS) or any other downlink reference signal.

At operation 1130, the process 1100 includes transmitting, by the base station and to the user device, the reference signal using the tone pattern. In some cases, the tone pattern can be transmitted to the user equipment over one or more of a Physical Downlink Control Channel (PDCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer. In some cases, the user equipment can receive, using a transceiver and from the base station, the reference signal having the tone pattern (e.g., over resource elements corresponding to the tone pattern).

With various examples of tone pattern optimization described above with reference to FIGS. 4-11 , FIG. 12 will now be described illustrating components of the UE 104.

FIG. 12 illustrates an example of a computing system 1270 of a user equipment (UE) 1207. UE 1207 may be the same as the UE 104 described above with reference to FIGS. 1-10 . In some examples, the UE 1207 can include a mobile phone, router, tablet computer, laptop computer, tracking device, wearable device (e.g., a smart watch, glasses, an XR device, etc.), Internet of Things (IoT) device, and/or other device used by a user to communicate over a wireless communications network. The computing system 1270 includes software and hardware components that can be electrically coupled via a bus 1289 (or may otherwise be in communication, as appropriate). For example, the computing system 1270 includes one or more processors 1284. The one or more processors 1284 can include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, dedicated hardware, any combination thereof, and/or other processing device or system. The bus 1289 can be used by the one or more processors 1284 to communicate between cores and/or with the one or more memory devices 1286.

The computing system 1270 may also include one or more memory devices 1286, one or more digital signal processors (DSPs) 1282, one or more subscriber identity modules (SIMs) 1274, one or more modems 1276, one or more wireless transceivers 1278, an antenna 1287, one or more input devices 1272 (e.g., a camera, a mouse, a keyboard, a touch sensitive screen, a touch pad, a keypad, a microphone, and/or the like), and one or more output devices 1280 (e.g., a display, a speaker, a printer, and/or the like).

The one or more wireless transceivers 1278 can transmit and receive wireless signals (e.g., signal 1288) via antenna 1287 to and from one or more other devices, such as one or more other UEs, network devices (e.g., base stations such as eNBs and/or gNBs, WiFi routers, etc.), cloud networks, and/or the like. As described herein, the one or more wireless transceivers 1278 can include a combined transmitter/receiver, discrete transmitters, discrete receivers, or any combination thereof. In some examples, the computing system 1270 can include multiple antennae. The wireless signal 1288 may be transmitted via a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), wireless local area network (e.g., a WiFi network), a Bluetooth™ network, and/or other network. In some examples, the one or more wireless transceivers 1278 may include an radio frequency (RF) front end including one or more components, such as an amplifier, a mixer (also referred to as a signal multiplier) for signal down conversion, a frequency synthesizer (also referred to as an oscillator) that provides signals to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, among other components. The RF front-end can generally handle selection and conversion of the wireless signals 1288 into a baseband or intermediate frequency and can convert the RF signals to the digital domain.

In some cases, the computing system 1270 can include a coding-decoding device (or CODEC) configured to encode and/or decode data transmitted and/or received using the one or more wireless transceivers 1278. In some cases, the computing system 1270 can include an encryption-decryption device or component configured to encrypt and/or decrypt data (e.g., according to the AES and/or DES standard) transmitted and/or received by the one or more wireless transceivers 1278.

The one or more SIMs 1274 can each securely store an International Mobile Subscriber Identity (IMSI) number and a related key assigned to the user of the UE 1207. The IMSI and the key can be used to identify and authenticate the subscriber when accessing a network provided by a network service provider or operator associated with the one or more SIMs 1274. The one or more modems 1276 can modulate one or more signals to encode information for transmission using the one or more wireless transceivers 1278. The one or more modems 1276 can also demodulate signals received by the one or more wireless transceivers 1278 in order to decode the transmitted information. In some examples, the one or more modems 1276 can include a 4G (or LTE) modem, a 5G (or NR) modem, a Bluetooth™ modem, a modem configured for vehicle-to-everything (V2X) communications, and/or other types of modems. In some examples, the one or more modems 1276 and the one or more wireless transceivers 1278 can be used for communicating data for the one or more SIMs 1274.

The computing system 1270 can also include (and/or be in communication with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 1286), which can include, without limitation, local and/or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device such as a RAM and/or a ROM, which can be programmable, flash-updateable and/or the like. Such storage devices may be configured to implement any appropriate data storage, including without limitation, various file systems, database structures, and/or the like.

In various embodiments, functions may be stored as one or more computer-program products (e.g., instructions or code) in memory device(s) 1286 and executed by the one or more processor(s) 1284 and/or the one or more DSPs 1282. The computing system 1270 can also include software elements (e.g., located within the one or more memory devices 1286), including, for example, an operating system, device drivers, executable libraries, and/or other code, such as one or more application programs, which may comprise computer programs implementing the functions provided by various embodiments, and/or may be designed to implement methods and/or configure systems, as described herein.

Specific details are provided in the description above to provide a thorough understanding of the embodiments and examples provided herein, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative embodiments of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Individual embodiments may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, and source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bitstream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Those of skill in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, in some cases depending in part on the particular application, in part on the desired design, in part on the corresponding technology, etc.

The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.

The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods, algorithms, and/or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves.

The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.

The foregoing description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example embodiments will provide those skilled in the art with an enabling description for implementing an example embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the application as set forth in the appended claims.

The terms “exemplary” and/or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and/or “example” is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term “aspects of the disclosure” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.

One of ordinary skill will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology used herein can be replaced with less than or equal to (“δ”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description.

Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.

The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly.

Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” or “at least one of A or B” can mean A, B, or A and B, and can additionally include items not listed in the set of A and B.

Illustrative aspects of the disclosure include:

Aspect 1: A method including: determining, by a user equipment, one or more parameters associated with communications with a base station over a time duration; determining, by the user equipment and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and transmitting the tone pattern to the base station.

Aspect 2: The method of aspect 1, wherein the tone pattern is determined from a plurality of existing tone patterns for communication of the reference signal.

Aspect 3: The method of any one of aspects 1 or 2, wherein the tone pattern is formed by an irregular arrangement of resource elements in a resource block for communication of the reference signal.

Aspect 4: The method of any one of aspects 1 to 3, further including receiving, from the base station, the reference signal having the tone pattern.

Aspect 5: The method of any one of aspects 1 to 4, wherein the tone pattern is determined using a machine learning model.

Aspect 6: The method of any one of aspects 1 to 5, wherein the machine learning model outputs the tone pattern based on the one or more parameters.

Aspect 7: The method of any one of aspects 1 to 6, further including retraining the machine learning model upon detection of a triggering condition.

Aspect 8: The method of any one of aspects 1 to 7, wherein the triggering condition is at least one of a change in a mobility status of the user equipment and a channel estimation error.

Aspect 9: The method of any one of aspects 1 to 8, wherein the reference signal is Channel State Information-Reference Signal (CSI-RS).

Aspect 10: The method of any one of aspects 1 to 9, wherein the reference signal is a Tracking Reference Signal (TRS).

Aspect 11: The method of any one of aspects 1 to 10, wherein the reference signal is a downlink reference signal.

Aspect 12: The method of any one of aspects 1 to 11, wherein transmitting the tone pattern comprises transmitting one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed.

Aspect 13: The method of any one of aspects 1 to 12, wherein the tone pattern is transmitted to the base station over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer.

Aspect 14: The method of any one of aspects 1 to 13, wherein the one or more parameters include at least a location of the user equipment, an environmental condition associated with the location, and a mobility status of the user equipment.

Aspect 15: An apparatus including one or more memories having computer-readable instructions stored therein and one or more processors. The one or more processors are configured to execute the computer-readable instructions to: determine one or more parameters associated with communications with a base station over a time duration; determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration; and transmit the tone pattern to the base station.

Aspect 16: The apparatus of aspect 15, wherein the tone pattern is determined from a plurality of existing tone patterns for communication of the reference signal.

Aspect 17: The apparatus of any one of aspects 15 or 16, wherein the tone pattern is formed by an irregular arrangement of resource elements in a resource block for communication of the reference signal.

Aspect 18: The apparatus of any one of aspects 15 to 17, wherein the one or more processors are further configured to execute the computer-readable instructions to receive, from the base station, the reference signal having the tone pattern.

Aspect 19: The apparatus of any one of aspects 15 to 18, wherein the one or more processors are further configured to execute the computer-readable instructions to determine the tone pattern using a machine learning model.

Aspect 20: The apparatus of any one of aspects 15 to 19, wherein the machine learning model is configured to output the tone pattern based on the one or more parameters.

Aspect 21: The apparatus of any one of aspects 15 to 20, wherein the one or more processors are further configured to execute the computer-readable instructions to retrain the machine learning model upon detection of a triggering condition.

Aspect 22: The apparatus of any one of aspects 15 to 21, wherein the triggering condition is at least one of a change in a mobility status of the apparatus and a channel estimation error.

Aspect 23: The apparatus of any one of aspects 15 to 22, wherein the reference signal is Channel State Information-Reference Signal (CSI-RS).

Aspect 24: The apparatus of any one of aspects 15 to 23, wherein the reference signal is a Tracking Reference Signal (TRS).

Aspect 25: The apparatus of any one of aspects 15 to 24, wherein the reference signal is a downlink reference signal.

Aspect 26: The apparatus of any one of aspects 15 to 25, wherein the one or more processors are configured to execute the computer-readable instructions to transmit the tone pattern by transmitting one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed.

Aspect 27: The apparatus of any one of aspects 15 to 26, wherein the tone pattern is transmitted to the base station over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer.

Aspect 28: The apparatus of any one of aspects 15 to 27, wherein the one or more parameters include at least a location of the apparatus, an environmental condition associated with the location, and a mobility status of the apparatus.

Aspect 29: One or more non-transitory computer-readable media includes computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform operations according to any of aspects 1 to 14.

Aspect 30: An apparatus including means for performing operations according to any of aspects 1 to 14.

Aspect 31: A method including: determining, by a base station, one or more parameters associated with communications with a user device over a time duration; determining, by the base station and based on the one or more parameters, a tone pattern for a reference signal for use in communications with the user device over a future time duration; and transmitting, by the base station and to the user device, the reference signal using the tone pattern.

Aspect 32: The method of any of aspects 31, wherein the tone pattern is determined from a plurality of existing tone patterns for communication of the reference signal.

Aspect 33: The method of any one of aspects 31 or 32, wherein the tone pattern is formed by an irregular arrangement of resource elements in a resource block for communication of the reference signal.

Aspect 34: The method of any one of aspects 31 to 33, further including receiving, the tone pattern from the user device.

Aspect 35: The method of any one of aspects 31 to 34, wherein receiving the tone pattern comprises receiving one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed.

Aspect 36: The method of any one of aspects 31 to 35, wherein the tone pattern is received over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer.

Aspect 37: The method of any one of aspects 31 to 36, wherein the tone pattern is determined using a machine learning model.

Aspect 38: The method of any one of aspects 31 to 37, wherein the machine learning model outputs the tone pattern based on the one or more parameters.

Aspect 39: The method of any one of aspects 31 to 38, wherein the reference signal is Channel State Information-Reference Signal (CSI-RS).

Aspect 40: The method of any one of aspects 31 to 39, wherein the reference signal is a Tracking Reference Signal (TRS).

Aspect 41: The method of any one of aspects 31 to 40, wherein the reference signal is a downlink reference signal.

Aspect 42: The method of any one of aspects 31 to 41, wherein the one or more parameters include at least a location of the user equipment, an environmental condition associated with the location, and a mobility status of the user equipment.

Aspect 43: An apparatus including one or more memories having computer-readable instructions stored therein and one or more processors. The one or more processors are configured to perform operations according to any of aspects 31 to 42.

Aspect 44: One or more non-transitory computer-readable media includes computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform operations according to any of aspects 31 to 42.

Aspect 45: An apparatus including means for performing operations according to any of aspects 31 to 42. 

What is claimed is:
 1. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: receive a first reference signal having a tone pattern, wherein the tone pattern is from a plurality of existing tone patterns for communication of one or more reference signals; determine, based on at least the first reference signal having the tone pattern, a predicted tone pattern for one or more reference signals for use in communications with a network device over a future time duration; and cause information corresponding to the predicted tone pattern to be transmitted to the network device.
 2. The apparatus of claim 1, wherein the at least one processor is configured to: receive, from the network device, a second reference signal having the predicted tone pattern.
 3. The apparatus of claim 1, wherein the predicted tone pattern includes an irregular arrangement of resource elements in a resource block.
 4. The apparatus of claim 1, wherein, to cause the information corresponding to the predicted tone pattern to be transmitted, the at least one processor is configured to cause one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed to be transmitted.
 5. The apparatus of claim 1, wherein the information corresponding to the predicted tone pattern is transmitted to the network device over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer.
 6. The apparatus of claim 1, wherein the at least one processor is configured to determine the predicted tone pattern further based on one or more parameters associated with communications with the network device over a time duration preceding the future time duration.
 7. The apparatus of claim 6, wherein the one or more parameters include at least a location of the apparatus, an environmental condition associated with the location, and a mobility status of the apparatus.
 8. The apparatus of claim 1, wherein the at least one processor is configured to determine the predicted tone pattern using a machine learning model.
 9. The apparatus of claim 8, wherein the machine learning model is configured to output the predicted tone pattern based on at least the first reference signal having the tone pattern.
 10. The apparatus of claim 9, wherein the machine learning model is configured to output the predicted tone pattern further based on one or more parameters associated with communications with the network device over a time duration preceding the future time duration.
 11. The apparatus of claim 8, wherein the machine learning model is trained using a training dataset, the training dataset including at least one of channel conditions, different tone patterns used for at least one of transmitting or receiving reference signals, measurements made by at least one user equipment (UE) based on the channel conditions and the different tone patterns, or a measured throughput.
 12. The apparatus of claim 11, wherein the channel conditions include at least one of indoor channel conditions, outdoor channel conditions, a mobility status of the at least one UE, or multipath channel characteristics.
 13. The apparatus of claim 1, wherein the first reference signal is a Channel State Information-Reference Signal (CSI-RS).
 14. The apparatus of claim 1, wherein the first reference signal is a Tracking Reference Signal (TRS).
 15. The apparatus of claim 1, wherein the first reference signal is a downlink reference signal.
 16. A method of wireless communication performed by a user equipment (UE), the method comprising: receiving a first reference signal having a tone pattern, wherein the tone pattern is from a plurality of existing tone patterns for communication of one or more reference signals; determining, based on at least the first reference signal having the tone pattern, a predicted tone pattern for one or more reference signals for use in communications with a network device over a future time duration; and transmitting information corresponding to the predicted tone pattern to the network device.
 17. The method of claim 16, further comprising: receiving, from the network device, a second reference signal having the predicted tone pattern.
 18. The method of claim 16, wherein the predicted tone pattern includes an irregular arrangement of resource elements in a resource block.
 19. The method of claim 16, wherein transmitting the information corresponding to the predicted tone pattern comprises transmitting one or more identified symbols and one or more locations within a resource block for the one or more identified symbols to be placed.
 20. The method of claim 16, wherein the information corresponding to the predicted tone pattern is transmitted to the network device over one or more of a Physical Uplink Control Channel (PUCCH), MAC Control Element (MAC-CE), or Radio Resource Control layer.
 21. The method of claim 16, wherein the predicted tone pattern is determined further based on one or more parameters associated with communications with the network device over a time duration preceding the future time duration.
 22. The method of claim 21, wherein the one or more parameters include at least a location of the user equipment, an environmental condition associated with the location, and a mobility status of the user equipment.
 23. The method of claim 16, wherein the predicted tone pattern is determined using a machine learning model.
 24. The method of claim 23, wherein the machine learning model is configured to output the predicted tone pattern based on at least the first reference signal having the tone pattern.
 25. The method of claim 24, wherein the machine learning model is configured to output the predicted tone pattern further based on one or more parameters associated with communications with the network device over a time duration preceding the future time duration.
 26. The method of claim 23, wherein the machine learning model is trained using a training dataset, the training dataset including at least one of channel conditions, different tone patterns used for at least one of transmitting or receiving reference signals, measurements made by at least one user equipment (UE) based on the channel conditions and the different tone patterns, or a measured throughput.
 27. The method of claim 26, wherein the channel conditions include at least one of indoor channel conditions, outdoor channel conditions, a mobility status of the at least one UE, or multipath channel characteristics.
 28. The method of claim 16, wherein the first reference signal is a Channel State Information-Reference Signal (CSI-RS).
 29. The method of claim 16, wherein the first reference signal is a Tracking Reference Signal (TRS).
 30. The method of claim 16, wherein the first reference signal is a downlink reference signal. 